I have two questions that I hope someone can answer.
1) The first is about SNR. Following an older post that I made on SNR
(left unaswered ) I will attempt to rephrase the question, hoping to
hear back from someone.
I am still wondering about how to best use the option --snr in
mne_make_movie. I have played around with the data a bit and changing
the SNR value seems to make a difference in the stc files.
So what is the best way to chose an SNR?
a) use the default value (3) independent of whether this is reflected in
your data
b) estimate the mean SNR value (over the epoch time), which will be
different for each subject and condition?
At the moment, I have calculated the SNR for each subject and condition.
The SNR value is between 1 and 2 and it is different for subjects and
conditions. Will that be a problem when I need to do group-level
statistics comparing different conditions?
2) The second question is about PCA. Is there any function in MNE that
would allow one to do a PCA on the *.fif files to remove eye-blinks or
cardiovascular related oscillations?
When using mne_analyze to calculate timecourses (either by clicking on
a vertex or using labels), how does MNE determine what the timecourse
should be? Thanks -
This is just the product of the data and the inverse solution. The
inverse solution specifies how to take the data from sensor space to
source space (i.e. vertices/labels). And the data is in sensor space
with multiple time points. Have I missed anything about your question?
Dan
?
Hello folks -
When using mne_analyze to calculate timecourses (either by clicking
on a vertex or using labels), how does MNE determine what the
timecourse should be? Thanks -
Eliezer Kanal
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1. I think the lack of reply generally reflects people's tendency to
leave it alone. Please people chime in, if I'm wrong.
2. There is no function in MNE that does a PCA. The closest thing
within MNE would be the ability of mne_browse_raw to create SSP
projections. However, I would not recommend using this for that
purpose; again please chime in, if you disagree. The next best
possibility to work on this would be to use the excellent MATLAB
toolbox to read the fif files into matlab remove the eyeblinks/
cardiovascular artifacts using whatever technique you like best and
save the data back and continue processing. Just be very careful
with your noise covariance matrices and your artifact extraction as
many of these tools have dangerous consequences for the true brain data.
Dan
?
Dear MNE users,
I have two questions that I hope someone can answer.
1) The first is about SNR. Following an older post that I made on
SNR (left unaswered ) I will attempt to rephrase the question,
hoping to hear back from someone.
I am still wondering about how to best use the option --snr in
mne_make_movie. I have played around with the data a bit and
changing the SNR value seems to make a difference in the stc files.
So what is the best way to chose an SNR?
a) use the default value (3) independent of whether this is
reflected in your data
b) estimate the mean SNR value (over the epoch time), which will be
different for each subject and condition?
At the moment, I have calculated the SNR for each subject and
condition. The SNR value is between 1 and 2 and it is different for
subjects and conditions. Will that be a problem when I need to do
group-level statistics comparing different conditions?
2) The second question is about PCA. Is there any function in MNE
that would allow one to do a PCA on the *.fif files to remove eye-
blinks or cardiovascular related oscillations?
Thank you in advance for the help,
Marina
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Mne_analysis mailing list
Mne_analysis at nmr.mgh.harvard.edu Mne_analysis Info Page
Marina
1. The snr issue is actually a very deep one. You are telling MNE how to set
up the regularization parameter, and yes, it will change the resulting
signals. The reason that happens is that there are many more brain states
than there are MEG sensor states. (this is that "ill-posed problem" thing).
As a result, we do a pragmatic thing, which is simplify the problem and make
it solvable - we add the constraint of minimizing the current at every point
in the brain (loosely described). But when we do that, we need to make a
somewhat arbitrary choice about how much weight to give that constraint.
Because there is no simply physical interpretation of what we are doing,
physiology cannot easily guide us here. In practice, I have no suggestion. I
defer to Matti.
2. I did some reading on the subject of artifact removal a few years ago. I
haven't kept up with the literature there. But I recall reading a relatively
compelling argument against using PCA for artifact removal that Dan hinted
at. Essentially PCA does not care if it also removes real brain data along
with artifact. One possible approach suggested out there was a related
technique, called ICA. I believe someone has written a matlab toolbox for
that. But the results there are still sometimes suspicious. If you trust
SSS, that may be an extremely powerful method to removal any artifact not
coming from the head. As for eye blinks and movements - you could consider
adaptive filtering. Another possibility is to fit several dipoles at the
anatomical location of the eyes, and fit data to them, and remove them. In
my playing around with these issues very briefly, I didn't find one perfect
solution. But many papers are out there saying they did.
EEGLab, which is an extensive free Matlab package has an ICA
extension. What you should think about is whether trying to remove eye-
blinks, etc by using ICA is worthwhile. There is always a chance that
it will corrupt your data. It is safer and, in my view, a better
practice to simply collect more data and totally dump all the time
intervals with artifacts using a simple threshold artifact detector.
I agree with you Yury. The safe bet is to discard any data that seems to
have a big artifact.
I look forward to the day when we can include those data with confidence
because we have a proper method of accounting for the artifact precisely.
) I will attempt to rephrase the question, hoping to
) I will attempt to rephrase the question,